Systems and methods for real-time complex character animations and interactivity

ABSTRACT

Systems, methods, and non-transitory computer-readable media can receive virtual model information associated with a virtual deformable geometric model. The virtual model information comprises a complex rig comprising a plurality of transforms and a first plurality of vertices defined by a default model, and a simplified rig comprising a second plurality of transforms and a second plurality of vertices corresponding to the first plurality of vertices. The simplified rig and the complex rig are deformed based on an animation to be applied to the virtual deformable geometric model. A set of offset data is calculated. The set of offset data comprises, for each vertex in the first plurality of vertices, an offset between the vertex and a corresponding vertex in the second plurality of vertices. A compressed version of the set of offset data is exported to a real-time processing engine for real-time animation of the virtual deformable geometric model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/538,590, filed on Jul. 28, 2017 and entitled “SYSTEMS AND METHODSFOR COMPLEX CHARACTER ANIMATIONS AND INTERACTIVITY FOR VR AND AR,” whichis incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present technology relates to the field of digital characteranimation. More particularly, the present technology relates totechniques for real-time complex character animations and interactivity.

BACKGROUND

Virtual Reality (VR) and Augmented Reality (AR) are new mediums forentertainment and storytelling that enable content creators to immersethe viewer in ways that are not possible in other mediums. VR and AR arepowerful immersive platforms to tell engaging stories with charactersthat audiences can empathize with, much as one would experience inliterature or cinema. Digital characters, such as those used in VR andAR experiences, often begin as a neutralized 3D model (often called the“default model”). A character “rig” is a digital puppet that enablescharacter models to be animated so that they can move, locomote, andemote, all in a believable way, giving the illusion of life. Thecharacter rig takes static default character models as inputs and thenapplies a set of procedural modifications to these models based onanimation input controls (such as how many degrees the elbow is bent orhow much the mouth of a character is smiling), producing a deforming,expressive character that animates over time. The character rigtypically comprises a set of animation controls which drive anunderlying hierarchy or collection of skeletal joints or bones. Thisprocess is often referred to as the motion system of the character. Thendeformation layer(s) attach or bind the character models to theseanimated skeletons using a variety of techniques.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured toreceive virtual model information associated with a virtual deformablegeometric model. The virtual model information comprises a complex rigcomprising a plurality of transforms and a first plurality of verticesdefined by a default model, and a simplified rig comprising a secondplurality of transforms and a second plurality of vertices. The secondplurality of vertices correspond to the first plurality of verticesdefined by the default model. The simplified rig and the complex rig aredeformed based on an animation to be applied to the virtual deformablegeometric model. A set of offset data is calculated. The set of offsetdata comprises, for each vertex in the first plurality of vertices, anoffset between the vertex and a corresponding vertex in the secondplurality of vertices.

In an embodiment, the animation comprises a plurality of frames, and theset of offset data comprises, for each vertex in the first plurality ofvertices and for each frame in the plurality of frames, an offsetbetween the vertex and a corresponding vertex in the second plurality ofvertices.

In an embodiment, an indication is received that the animation is to beapplied to the virtual deformable geometric model in real-time in animmersive environment.

In an embodiment, the simplified rig is deformed in real-time based onthe animation, and a complex model deformation is generated based on thedeforming the simplified rig and the set of offset data.

In an embodiment, the second plurality of transforms represents a subsetof the first plurality of transforms.

In an embodiment, the first plurality of transforms comprise a pluralityof deformation transforms and a plurality of control transforms, and thesecond plurality of transforms comprises a subset of the plurality ofdeformation transforms.

In an embodiment, at least some of the first plurality of transforms arearranged hierarchically.

In an embodiment, at least some of the second plurality of transformsare arranged hierarchically.

In an embodiment, the simplified rig further comprises a set of skinningweights that define a relationship between the second plurality oftransforms and the second plurality of vertices.

In an embodiment, each vertex in the second plurality of vertices isassociated with no more than four transforms of the second plurality oftransforms.

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured toreceive virtual model information associated with a virtual deformablegeometric model. The virtual model information comprises a complex rigcomprising a plurality of transforms and a first plurality of verticesdefined by a default model, and a simplified rig comprising a secondplurality of transforms and a second plurality of vertices. The secondplurality of vertices correspond to the first plurality of verticesdefined by the default model. The simplified rig and the complex rig aredeformed based on an animation to be applied to the virtual deformablegeometric model. A set of offset data is calculated. The set of offsetdata comprises, for each vertex in the first plurality of vertices, anoffset between the vertex and a corresponding vertex in the secondplurality of vertices. A compressed version of the set of offset data isexported to a real-time processing engine for real-time animation of thevirtual deformable geometric model.

In an embodiment, the compression version of the set of offset data isgenerated.

In an embodiment, the generating the compressed version of the set ofoffset data comprises calculating a tight bounding box for each vertexof the first plurality of vertices by, for each vertex of the firstplurality of vertices, tracking minimum and maximum X, Y, and Z valuesof offsets for the vertex through the animation.

In an embodiment, the generating the compressed version of the set ofoffset data further comprises quantizing offsets in the set of offsetdata using integers of 16-bits or less based on the tight bounding boxesto generate a lower bit quantization of offset data.

In an embodiment, the generating the compressed version of the set ofoffset data further comprises combining the lower bit quantization ofoffset data with a video compression technique by mapping X, Y, and Zoffset values to color component values.

In an embodiment, the generating the compressed version of the set ofoffset data further comprises combining the lower bit quantization ofoffset data with lossy or lossless audio compression techniques bymapping the X, Y, and Z offset values to channels of an audio stream.

In an embodiment, the generating the compressed version of the set ofoffset data further comprises combining the lower bit quantization ofoffset data with lossy or lossless photo compression techniques bymapping the X, Y, and Z offset values to the color component values ofpixels in a photo and using adjacent pixels for sequential animationframes.

In an embodiment, the generating the compressed version of the set ofoffset data further comprises correlating each vertex of the firstplurality of vertices to a pixel of a video stream.

In an embodiment, the correlating each vertex of the first plurality ofvertices to a pixel of a video stream comprises correlating each vertexof the first plurality of vertices to a pixel of a video stream bymapping each vertex of the first plurality of vertices to a texturelocation using a texture lookup or by using an indexing scheme.

In an embodiment, the generating the compressed version of the set ofoffset data comprises clustering the set of offset data to generate aplurality of clusters, and further wherein the clustering the set ofoffset data comprises clustering the set of offset data using K-means.

In an embodiment, the generating the compressed version of the set ofoffset data comprises clustering the set of offset data to generate aplurality of clusters; and applying Principal Component Analysis (PCA)to each cluster of the plurality of clusters.

In an embodiment, the generating the compressed version of the set ofoffset data comprises representing changes across time for offsets ofthe set of offset data using an approximating parametrized analyticalfunction; and retaining only a subset of the parameters of theapproximating parametrized analytical function

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured toidentify a virtual deformable geometric model to be animated in areal-time immersive environment. The virtual deformable geometric modelcomprises a virtual model mesh comprising a plurality of vertices, aplurality of edges, and a plurality of faces. The virtual model mesh isiteratively refined in one or more iterations to generate a refinedmesh. Each iteration of the one or more iterations increases the numberof vertices, the number of edges, and/or the number of faces. Therefined mesh is presented during real-time animation of the virtualdeformable geometric model within the real-time immersive environment.

In an embodiment, the real-time immersive environment is a virtualreality environment or an augmented reality environment.

In an embodiment, the refined mesh comprises a second plurality ofvertices, and each vertex of the second plurality of vertices is alinear weighted combination of vertices in the plurality of vertices.

In an embodiment, each linear weighted combination of vertices comprisesone or more weights, and the method further comprises pre-computing theone or more weights for each linear weighted combination prior toreal-time animation of the virtual deformable geometric model within thereal-time immersive environment.

In an embodiment, each linear weighted combination is limited to apre-defined maximum number of weights.

In an embodiment, for each vertex of the second plurality of verticeshaving a plurality of weights greater than the pre-defined maximumnumber of weights, a subset of weights is selected from the plurality ofweights for inclusion in the linear weighted combination based onabsolute magnitudes of the weights.

In an embodiment, an approximate normal vector and an approximatetangent vector is calculated for each vertex in the second plurality ofvertices.

In an embodiment, each vertex in a first subset of vertices of thesecond plurality of vertices has exactly four adjacent vertices; thecalculating the approximate normal vector for each vertex of the firstsubset of vertices comprises, for each vertex in the first subset ofvertices, calculating an approximate normal vector based on the fourvertices adjacent to the vertex; and the calculating the approximatetangent vector for each vertex of the first subset of verticescomprises, for each vertex in the first subset of vertices, calculatingan approximate tangent vector based on the four vertices adjacent to thevertex.

In an embodiment, each vertex in a first subset of vertices of thesecond plurality of vertices has greater than four adjacent vertices;the calculating the approximate normal vector for each vertex of thefirst subset of vertices comprises, for each vertex of the first subsetof vertices, selecting four vertices adjacent to the vertex, andcalculating an approximate normal vector based on the selected fourvertices adjacent to the vertex; and the calculating the approximatetangent vector for each vertex of the first subset of verticescomprises, for each vertex in the first subset of vertices, calculatingan approximate tangent vector based on the selected four verticesadjacent to the vertex.

In an embodiment, each vertex in a first subset of vertices of thesecond plurality of vertices has fewer than four adjacent vertices; thecalculating the approximate normal vector for each vertex of the firstsubset of vertices comprises, for each vertex of the first subset ofvertices, determining a set of four vertices associated with the vertex,the set of four vertices comprising all vertices adjacent to the vertexand the vertex itself substituted for any missing vertices, andcalculating an approximate normal vector for the vertex based on the setof four vertices; and the calculating the approximate tangent vector foreach vertex of the first subset of vertices comprises, for each vertexin the first subset of vertices, calculating an approximate tangentvector based on the set of four vertices.

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured toidentify a virtual character being presented to a user within areal-time immersive environment. A first animation to be applied to thevirtual character is determined. A nonverbal communication animation tobe applied to the virtual character simultaneously with the firstanimation is determined. The virtual character is animated in real-timebased on the first animation and the nonverbal communication animation.

In an embodiment, the nonverbal communication animation is determinedbased on a user input associated with the user.

In an embodiment, the user input comprises location and orientationinformation associated with a set of feature locations on the user thatare tracked in the immersive environment.

In an embodiment, the nonverbal communication animation comprises alooking animation to cause the virtual character to look at one or morefeature locations of the set of feature locations on the user based onthe location and orientation information.

In an embodiment, the nonverbal communication animation comprises alooking animation to cause the virtual character to look alternatinglyat two or more objects in the real-time immersive environment, whereinthe two or more objects include at least two of: a head of the user, ahand of the user, a feature location on the body or face of the user,another user in the real-time immersive environment, a second virtualcharacter in the real-time immersive environment, or a simulated objectin the real-time immersive environment.

In an embodiment, the location and orientation information compriseshead location and orientation information associated with the head ofthe user, and the nonverbal communication animation comprises amirroring animation to cause the virtual character to mirror headmovements by the user based on the head location and orientationinformation.

In an embodiment, the location and orientation information furthercomprises hand location and orientation information associated with oneor more of the user's hands.

In an embodiment, the animating the virtual character in real-time basedon the first animation and the nonverbal communication animationcomprises layering the nonverbal communication animation with the firstanimation.

In an embodiment, the nonverbal communication animation is determinedbased on virtual environment state information associated with theimmersive environment.

In an embodiment, the nonverbal communication animation comprisesincreasing an activity level of the virtual character based on thevirtual environment state information.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an example system including a character animationmodule, according to an embodiment of the present disclosure.

FIG. 1B illustrates a block diagram representation of an example ofanimation clip data, according to an embodiment of the presentdisclosure.

FIG. 2 illustrates an example data exporter module, according to anembodiment of the present disclosure.

FIG. 3 illustrates an example nonverbal character cues module, accordingto an embodiment of the present disclosure.

FIG. 4 illustrates an example complex model deformation module,according to an embodiment of the present disclosure.

FIG. 5 illustrates an example model tessellation module, according to anembodiment of the present disclosure.

FIG. 6 illustrates an example scenario associated with modeltessellation, according to an embodiment of the present disclosure.

FIG. 7 illustrates an example scenario associated with approximation ofnormal vectors, according to an embodiment of the present disclosure.

FIG. 8 illustrates an example scenario associated with approximation ofnormal vectors, according to an embodiment of the present disclosure.

FIG. 9 illustrates an example scenario associated with approximation ofnormal vectors, according to an embodiment of the present disclosure.

FIG. 10 illustrates an example flow block diagram for the generation ofsubdivision surfaces, according to an embodiment of the presentdisclosure.

FIG. 11 illustrates an example method to calculate vertex offset databetween the vertices of a complex and a simple character model,according to an embodiment of the present disclosure.

FIG. 12 illustrates an example method to calculate compressed vertexoffset data between the vertices of a complex and a simple charactermodel, according to an embodiment of the present disclosure.

FIG. 13 illustrates an example method to generate a refined mesh from abase mesh, according to an embodiment of the present disclosure.

FIG. 14 illustrates an example method to augment virtual characteranimation with nonverbal communication animation, according to anembodiment of the present disclosure.

FIG. 15 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION Real-Time Complex Model Animations andInteractivity

Virtual Reality (VR) and Augmented Reality (AR) are new mediums forentertainment and storytelling that enable content creators to immersethe viewer in ways that are not possible in other mediums. VR and AR arepowerful immersive platforms to tell engaging stories with charactersthat audiences can empathize with, much as one would experience inliterature or cinema. However, unlike film experiences, VR and ARprovide the opportunities for the digital characters to interact withand react to the viewer in real time because the viewer is fullyimmersed in the virtual world of the story.

Digital characters often begin as a neutralized 3D model (often calledthe “default model”). A character “rig” is a digital puppet that enablescharacter models to be animated so that they can move, locomote, andemote, all in a believable way, giving the illusion of life. Thecharacter rig takes static default character models as inputs and thenapplies a set of procedural modifications to these models based onanimation input controls (such as how many degrees the elbow is bent orhow much the mouth of a character is smiling), producing a deforming,expressive character that animates over time. The character rigtypically comprises a set of animation controls which drive anunderlying hierarchy or collection of skeletal joints or bones. Thisprocess is often referred to as the motion system of the character. Thendeformation layer(s) attach or bind the character models to theseanimated skeletons using a variety of techniques.

Conventional approaches to character animation have generally involveddifferent levels of quality for real-time applications (such as videogames, VR, and/or AR) and non-real-time applications (such as featurefilms). It can be appreciated that computing constraints inherent inmost real-time applications have resulted in real-time applicationsgenerally utilizing lower quality animations than non-real-timeapplications. Video games will typically render a single viewpoint at 30or 60 frames per second (FPS). VR projects have even greater computingrequirements, as they will generally render two viewpoints (one for eacheye) at 90 FPS. As such, conventional approaches to VR and AR, as wellas most video games, use variations of skinning and morph targets, whichare simple character deformation techniques that result in lower qualitycharacter animations, but are optimized to run in real-time. Incharacter rigs intended for real-time applications, “skinning” is atechnique commonly used to bind the default model's skin to the rig'sbones. Each bone is associated with a set of vertices on the skin thatare affected by the movement of the bone. Each skin vertex can beaffected by multiple bones. Character rigs for real-time applicationstypically place limits on the number of bones that each skin vertex canbe associated with as well as the number of overall bones in the rig, sothat the character can be deformed in real-time. In real-time characterrigs, facial performances are often captured as a set of blend shapes ormorph targets. A face rig can consist of a number of modeled face shapes(such as a smile pose) that are blended on or off by the animator.

Conversely, in a character rig for feature films, the constraintsassociated with real-time animation are not applicable and, as such, awider range of techniques can be applied to produce the animatedcharacter models. A more sophisticated set of deformers as well asmultiple deformation layers may be employed to capture the complexitiesof musculature, fat, and skin or to maintain specific visual qualitiesof the shape or silhouette of a character. Facial performances are notlimited to blend shapes and may contain many deformation layers tocapture all the intricacies of the face. In many cases, complexcharacter rigs may run physically-based simulations to more accuratelydepict long hair, clothing, muscles, and skin sliding on top of anatomy.Complex feature-film rigs do not need to run in real-time and it istypical to compute the final character models offline using adistributed cluster of computers, especially with simulated elements.Due to restrictions on computing resources inherent in real-timeprocessing, conventional approaches to character animation in real-timeapplications, such as gaming and VR, result in animations that lack thequality, complexity, and richness of character animations innon-real-time applications such as feature films.

An improved approach rooted in computer technology overcomes theforegoing and other disadvantages associated with conventionalapproaches specifically arising in the realm of computer technology. Ingeneral, the present technology provides an innovative system designedfor any simulated immersive environment, which includes VR and AR butalso extends to other immersive scenarios such as holographictechnologies and brain-computer interfaces. Immersive environments maycollectively be referred to as VR for simplicity, as appropriate. Thepresent technology can be applicable to many domains such asentertainment, medical, training, and education contexts. Although manyof the features described herein will be described with reference to VRand AR entertainment, it will be understood that the present technologycan be applied to any simulated or immersive environment with a broadrange of applications beyond entertainment, and the features discussedherein represent one specific example application. In variousembodiments, the present technology provides an innovative systemdesigned for VR and AR entertainment (which may collectively be referredto as VR for simplicity, as appropriate) that creates high-qualityanimations, including character animations, with all the visual fidelityand acting subtlety that one would expect from a feature film, or othernon-real-time animation applications, but can run on VR computingdevices in real-time. The present technology enables VR characterperformances that are both highly expressive and are highly responsiveto user inputs. The present technology can be the backbone for creatingtruly immersive VR experiences that place the user inside the story,where the user is a character and can interact with other characters andparticipate in the narrative. The present technology enables VRexperiences that combine the empathy of a film with the agency of a gamebut with the motivations of real life.

Some benefits of the present technology compared to conventionalapproaches include:

-   -   high quality virtual representations of virtual deformable        geometric models, including both characters and other aspects of        a virtual environment with simulated movement;    -   high quality character performances, both in visual fidelity and        animation (movement) quality, rendered in real-time for VR, that        can be responsive to user inputs;    -   feature film quality facial performances running in real-time VR        experiences that can dynamically respond to user input;    -   character simulations for skin, cloth, and hair running in        real-time VR experiences;    -   smooth subdivision surface models running in real-time VR        experiences;    -   character visual fidelity is not limited by fast but low-quality        techniques (such as skinning) that can run in real-time game        engines;    -   decoupling the character animation pipeline from the real-time        engine pipeline; and    -   the ability to run on VR headsets tethered to high-end        computers, as well as the ability to scale down to run on less        powerful devices such as mobile-driven VR headset devices, AR        devices, and other immersive technologies.

The present technology enables artists to create high-quality characteranimations with all the subtlety that one expects from a feature film,but running in real-time in a real-time engine where the results can beprocedurally modified by the real-time engine. The present technologyremoves restrictions on real-time animated character performances byallowing for arbitrarily complex character setups in the animationcreation tool. The disclosed real-time engine pipeline allows for fullcharacter interactivity by blending cycle animations together based ongame logic and user inputs to produce deformed complex character modelsthat are rendered as smooth subdivision surfaces instead of hard-edgedpolygonal models. More details relating to the disclosed technology areprovided below.

FIG. 1A illustrates an example system 100 including a characteranimation module 102, according to an embodiment of the presentdisclosure. The character animation module 102 can be implemented in oneor more software applications running on one or more computing devices.The components (e.g., modules, elements, etc.) shown in this figure andall figures herein are exemplary only, and other implementations mayinclude additional, fewer, integrated, or different components. Somecomponents may not be shown so as not to obscure relevant details. Invarious embodiments, one or more of the functionalities described inconnection with the character animation module 102 can be implemented inany suitable combinations.

As shown in the example of FIG. 1A, the character animation module 102can include an animation creation module 104 and a real-time enginemodule 116. As also shown in the example of FIG. 1A, the animationcreation module 104 can include a complex character rig module 106, asimplified character rig module 108, an animation module 110, and a dataexporter module 112. As further shown in the example of FIG. 1A, thereal-time engine module 116 can include a data import module 118, ananimation drivers module 120, a character interactive constraints module122, a nonverbal character cues module 124, a simplified character riganimation module 126, a complex model deformation module 128, a modeltessellation module 130, and a rendering pipeline module 132. In anembodiment, the complex model deformation module 128, the modeltessellation module 130, and/or the rendering pipeline 132 may beimplemented, in part or in whole, using a graphics processing unit (GPU)134.

In some embodiments, the various modules and/or applications describedherein can be implemented, in part or in whole, as software, hardware,or any combination thereof. In general, a module and/or an application,as discussed herein, can be associated with software, hardware, or anycombination thereof. In some implementations, one or more functions,tasks, and/or operations of modules and/or applications can be carriedout or performed by software routines, software processes, hardware,and/or any combination thereof. In some cases, the various modulesand/or applications described herein can be implemented, in part or inwhole, as software running on one or more computing devices or systems,such as on a user or client computing device or on a server. Forexample, one or more modules and/or applications described herein, or atleast a portion thereof, can be implemented as or within an application(e.g., app), a program, or an applet, etc., running on a user computingdevice or a client computing system. In another example, one or moremodules and/or applications, or at least a portion thereof, can beimplemented using one or more computing devices or systems that includeone or more servers, such as network servers or cloud servers. It shouldbe understood that there can be many variations or other possibilities.

The complex character rig module 106 can be configured to receive andmaintain data defining a complex character rig for a digital virtualcharacter. In general, a complex character rig can be associated withand/or include a default character model having a plurality of vertices.The complex character rig can further comprise a set of animationcontrols which drive an underlying set (e.g., hierarchy, collection) oftransforms, defining 3D positions and orientations. As previouslydescribed, these transforms are typically represented as skeletal jointsor bones in a character rig. The complex skeletal rig joints deform andmove the vertices in the default character model. In certainembodiments, skeletal joints can comprise 3D point positions (withoutorientation transform information). In certain embodiments, skeletaljoints can comprise a combination of point positions and fulltransforms. In certain embodiments, a collection of joints may beconstructed in a hierarchy. However, in other embodiments, a collectionof joints may not be arranged hierarchically. Furthermore, the presentlydisclosed technology can be applied to any arbitrary set of points andtransforms that drive geometric deformations. Although many of thefeatures described herein will be described with reference to characterdeformations driven by skeletal joints, it will be understood that thepresent technology can be applied to any deformable geometric modelsdriven by an arbitrary set of points and transforms, and thedeformations to virtual character models discussed herein represent onespecific example application. A rigging artist can create a complexcharacter rig that, in various embodiments, may utilize a variety oftechniques, including a range of deformers, multiple deformation layers,and physically-based simulations. In certain embodiments, a set ofdeformation joints are derived from the skeletal control joints andthese deformation joints drive the geometric deformation layers.Deformation joints provide a separation and encapsulation of thedeformation layers from the skeletal components. In certain embodiments,the complex character rig can employ multiple deformation layers tocapture the complexities of various materials such as, for example,musculature, fat, and skin, or to maintain specific visual qualities ofthe shape or silhouette of a character. In certain embodiments, thecomplex character rig contains multiple sets of deformation jointscorresponding to the various deformation layers. In certain embodiments,a combination of skeletal joints and deformation joints drive thegeometric deformations. The complex character rig can also includefacial performances that may contain many deformation layers to captureall the intricacies of the face. In various embodiments, the complexcharacter rig can run physically-based simulations to more accuratelydepict elements of the character, such as long hair, clothing, muscles,skin sliding on top of anatomy, and so forth. The complex character rigmay also incorporate sophisticated facial animation setups to captureboth exaggerated facial expressions and subtle details. The disclosedtechnology removes any restrictions on the character rigs by separatingthe front-end character setup, animation, and pipeline (i.e., theanimation creation module 104) from the real-time character systems thatrun in the real-time processing engine (i.e., the real-time enginemodule 116), as will be described in greater detail herein. As such, invarious embodiments, the complex character rig may not have specificdata requirements apart from comprising a default character model asinput and a joint hierarchy.

The simplified character rig module 108 can be configured to receive,maintain, and/or generate data defining a simplified character rig torepresent a digital virtual character. In an embodiment, the simplifiedcharacter rig can be associated with a complex character rig generated,received, and/or maintained by the complex character rig module 106. Inan embodiment, the simplified character rig can comprise a set ofskeletal joint transforms (akin to the skeletal joints in the complexcharacter rig). In various embodiments, the simplified skeletal jointtransforms may be defined independently from the complex character rig,and may or may not be hierarchical. In one embodiment, the simplifiedcharacter rig may be a simplified version of a complex character rig.For example, the simplified character rig may comprise a simplifiedskeleton representing a subset of skeletal joints present in the complexcharacter rig skeleton, and excluding all animation control data fromthe complex character rig skeleton (e.g., excluding one or more controljoints from the complex character rig skeleton). In an embodiment, asimplified character rig can comprise the simplified skeletal jointhierarchy, default model information defining a default character model(for example, this may be the same default character model included inthe complex character rig), a skinning weight map that defines therelationship between the simplified skeleton and the vertices in thedefault character model, and one or more blend shapes. In an embodiment,the simplified character rig can be a limited skinned character rig,wherein each vertex in the default character model may be attached to nomore than a maximum number of bones, e.g., no more than 4 bones, in thesimplified skeleton. In certain embodiments, the simplified characterrig can consist of more complex deformers in addition to skinning (suchas lattices or smoothing deformers) supported by the real-time enginemodule 116. In certain embodiments, the simplified character rig can becreated manually by a rigging artist. In certain embodiments, thesimplified character rig can be derived automatically from a complexcharacter rig by using statistical or machine learning methods. Forexample, a training set can be constructed by using calisthenicanimation poses with the targets being the deformed model controlvertices for the complex character rig. A cost function can be definedthat computes the difference between the complex deformed controlvertices and the simplified control vertices. The vertex attachments andthe export rig skinned weight maps can then be learned to minimize thiscost function

The animation module 110 can be configured to receive animation data fora complex character rig and/or a simplified character rig. Animation fora given project can be broken up into a series of shots or clips. Agiven shot or clip can include one or more virtual characters, eachrepresented by and/or associated with a complex character rig and asimplified character rig. For a given shot, an animator can hand-animateor keyframe the character rigs (complex and/or simplified) for the oneor more characters over time or the animation can be motion capturedfrom a live performance by an actor or a combination of multipletechniques. In certain embodiments, one shot can represent a singlecharacter action (which may also be referred to as an animation cycle orcycle animation). The animation module 110 can be configured to outputanimation data for one or more shots. The output animation data for ashot can include the simplified character's animated skeletal joint datafor each virtual character along with any other relevant scene data(such as locations of props and environment elements). Animating virtualcharacters with the present technology provides the important benefitthat, unlike conventional approaches to real-time character animation,there are no restrictions placed on the complex character rigs, asdescribed above. As such, animators are able to achieve much strongercharacter performances than in a conventional pipeline.

The data exporter module 112 can be configured to export character rigdata for a single or multiple character rigs and character animationdata 114 for a set of animations. The character rig data and thecharacter animation data 114 can be exported for use by a real-timeprocessing engine (e.g., real-time engine module 116). Character rigdata can include, for example, character default model information(e.g., vertex positions, mesh topology), joint hierarchy data,simplified character rig skin weight maps, data relating to anyadditional deformers for a simplified character rig, and the like. In anembodiment, character rig data can be exported once per character (i.e.,not per frame of animation).

In an embodiment, character animation data relating to one or moreanimations to be applied to virtual characters can be exported percharacter per frame of animation. Some examples of character animationdata can include local space animated joint transforms relative to thesimplified rig's joint hierarchy and character model control vertices.In an embodiment, the character model control vertices can includecomplex character mesh data that is stored as offset positions relativeto the mesh of a simplified character rig in a space that is a weightedaverage of skinned weight map joints of each vertex. As will bedescribed in greater detail below, offsets can be calculated for eachvertex in a character model for each frame in each animation. Theoffsets can be used by the real-time engine module 116 to effectuatedeformation of a complex character rig by deforming a simplifiedcharacter rig instead and then applying the offsets to the vertices inthe character model. The data exporter module 112 can either store thisdata locally on disk or send it to a remote server. Exporting per-framevertex offset data can lead to large datasets being stored on disk ortransferred over a network to a remote server. Various embodiments ofthe present technology provide for multiple forms of lossy or losslesscompression to reduce the export data size. Some examples of compressiontechniques can include reducing data over time by curve fitting vertexoffsets across animation frames or reducing a number of stored verticesby using statistical or machine learning techniques such as PrincipalComponent Analysis (PCA). More details regarding the data exportermodule 112 will be provided below with reference to FIG. 2.

The data import module 118 can be configured to import character rigdata and character animation data 114. In an embodiment, the data importmodule 118 can load the character rig data and character animation data114 exported by the data exporter module 112 on demand as needed via anasynchronous data manager. In various embodiments, the asynchronous datamanager can load the data from a local disk or from a remote server. Inan embodiment, the asynchronous data manager can import the characterrig data and character animation data into a real-time game engine(e.g., real-time engine module 116).

As noted previously, the character animation data can include asignificant amount of data, as it comprises data, such as offset data,for one or more characters for each frame of an animation. In anembodiment, the character animation data can be conditioned so that itcan be quickly consumed at runtime. FIG. 1B provides a block diagramrepresentation of how character animation data can be conditioned inthis way. As mentioned above, character animation data can comprise datapertaining to one or more animation clips. In the example of FIG. 1B, aset of animation clip data includes a simplified rig's joint animationdata (e.g., animated joint transforms for the simplified rig's jointhierarchy) and compressed vertex animation data (e.g., vertex offsetsfor each model control relative to the simplified rig). The compressedvertex animation data is sliced into small chunks (e.g., 256 frames)that can be streamed to a GPU (e.g., GPU 134) asynchronously duringruntime without stalling. This animation clip data can be fed from thelocal machine or streamed from cloud-based servers. In order to ensurethat streaming of character animation data to the GPU does not causehitches, the data import module 118 can implement a centralizedscheduler to queue and stream slices of animation as needed. Asmentioned above, character animation data can include data pertaining toa plurality of animation clips. In certain embodiments, a first chunk(e.g., 256 frames) of all animation clips can be streamed to GPU memoryat VR application startup. In certain embodiments, the data importmodule 118 can stream character animation data stored locally on a VRdevice for optimal load performance. This however can lead to a large VRapplication footprint (e.g., large local storage usage). Alternatively,the data import module 118 can stream the character animation data froma remote server. In certain embodiments, as needed during VR applicationuse, additional chunks can be fetched and stored locally in anticipationof being streamed to the GPU, while chunks no longer needed can bediscarded from local storage, in a manner that balances availability oflocal storage and streaming speed between the local machine andcloud-based servers.

Returning to FIG. 1A, the animation drivers module 120 can be configuredto drive animation cycles. Real-time processing engines provide a numberof mechanisms to drive or blend between different cycle animations. Forexample, timeline-based linear editing tools can be used to createnarrative experiences such as in-game cinematics. State machines canallow interactive games to blend between different cycles and createcomplex character behaviors based on user interactions. In certaininstances, engineers can also program procedural artificial intelligence(AI) for even more sophisticated character response.

The character interactive constraints module 122 can be configured toplace constraints on character rig joint locations. In order to blendbetween cycle animations and to create believable character responses,it may be desirable to place constraints on joint locations for acharacter. To blend between a set of animation cycles, the characterinteractive constraints module 122 can linearly interpolate between theset of joint hierarchies. To place additional constraints such as makinga character look at a specified location, the character interactiveconstraints module 122 can directly modify specific joint locations(such as the neck and head joints). The present technology also enablesa rich set of constraints including, for example, supporting regionallayering of joint hierarchies to effectuate layering together animationcycles (i.e., one on top of another).

The nonverbal character cues module 124 can be configured to modifyand/or adjust character rig joint locations based on user and sceneinputs. Humans use subconscious communication cues to build emotionalconnections. These nonverbal cues are critical to building meaningfulrelationships with each other in the real world. VR has the uniquecapabilities to immerse the viewer fully in an experience, to enable theviewer to be a character in the experience with a virtual embodiment,and to faithfully track the viewer's movements (e.g., using head andhand locations). These are key ingredients to building meaningfulsubconscious communication cues into a VR system and are not possible inconventional film or games. In an embodiment, the nonverbal charactercues module 124 can use user inputs, such as user head and hand inputs,to incorporate complex nonverbal character responses into VR animations.Example use cases include

1. Looking at a user/maintaining eye contact

2. Mirroring behavior of a user

3. Increased activity

4. Dynamic performances.

As mentioned, the nonverbal character cues module 124 can receive userinputs to generate nonverbal communication animations by modifying oneor more joints and/or vertices of a character rig. In an embodiment, thenonverbal character cues module 124 can receive user input data from aVR platform that tracks a user in real-time. For example, the nonverbalcharacter cues module 124 can use a head-mounted display (HMD) positionand orientation as well as hand controller position(s) andorientation(s) to accurately track the locations and orientations of thehead and hands of a user. In another embodiment, the nonverbal charactercues module 124 can receive additional tracked inputs such as datapertaining to the position and orientation of additional real-worldobjects that are incorporated into a VR experience. In anotherembodiment, the nonverbal character cues module 124 can receive fullbody and facial tracking for a user which provides more sophisticateduser inputs to the system. In another embodiment, the nonverbalcharacter cues module 124 can receive one or more user inputs from userswho express their emotions through assistive technologies such as tonguecontrollers, eye scanners, or brain-computer interfaces. In anotherembodiment, the nonverbal character cues module 124 can receive one ormore user inputs from multiple users in a shared social experience.

The nonverbal character cues module 124 can also be configured toreceive inputs from a VR scene and generate nonverbal communicationanimations based on scene inputs. For example, the nonverbal charactercues module 124 can utilize virtual character joint locations as inputsas well as other environmental inputs such as the location of variousprops in the VR scene. The system can also use state informationassociated with a VR experience.

Nonverbal communication animation data output by the nonverbal charactercues module 124 can cause nonverbal communication animations to belayered on a virtual character's animated performance based on user andscene inputs. For example, a source animation or base animation that avirtual character is to perform may be determined, and a separatenonverbal communication animation may also be determined. The sourceanimation may cause the virtual character to be animated in a firstmanner (e.g., may define a first set of orientations and/or locationsfor a set of joints of an animation rig). The nonverbal communicationanimation data can cause one or more joint locations of an animation rigto be further adjusted to create a modified set of joint locations. Forexample, a base animation may cause a virtual character to walk toward auser. A separate mirroring nonverbal communication animation may causethe virtual character to mirror the user while the virtual character iswalking toward the user. Or a separate looking nonverbal communicationanimation may cause the virtual character to maintain eye contact withthe user while the virtual character is walking toward the user.Procedural joint modification provides an intuitive and direct mechanismto modify existing animations. In another embodiment, one or morenonverbal communication animations and layers can be blended together.The blending or layering of animations can occur across all theanimation rig joints or only for a specific subset or region of joints.This approach enables artists to explicitly animate a nonverbalperformance. The nature of the blending can be arbitrarily complex. Moredetails regarding the character nonverbal communication module 124 willbe provided below with reference to FIG. 3.

The simplified character rig animation module 126 can be configured toreceive animation drivers (e.g., from the animation drivers module 120),interactive constraints (e.g., from the character interactiveconstraints module 122), and nonverbal communication animation data(e.g., from the nonverbal character cues module 124) as inputs, andanimate a simplified character rig joint hierarchy based on thoseinputs. The simplified character rig animation module 126 can receive aset of animation clips, blending weights, and times from the animationdrivers module 120. Character animation data for the set of animationclips can be received from the data import module 118. Modifications tothe animation data may be received from the nonverbal character cuesmodule 124. Next, the simplified character rig animation module 126 canupdate the simplified character rig joint hierarchy based on animationsand blending. Finally, the simplified character rig animation module 126can modify joint locations based on the interactive constraints receivedfrom the character interactive constraints module 122.

The simplified character rig animation module 126 can be configured totrigger, blend, and/or layer one or more animations in a real-timeengine. This may be achieved in a variety of ways, including through theuse of state machines, nonlinear timeline tools, and/or event-basedtriggers. Animations can be combined and blended wholly, or partiallyusing predefined masks. This provides the ability to create layeredperformances. For instance, facial performance or hand gestures may bemodified based on user input or actions. The simplified character riganimation module 126 can blend local joint transforms and calculateweights of vertex offsets to drive the complex model deformation module128 (discussed in greater detail below).

The complex model deformation module 128 can be configured to computefinal character model deformations based on the simplified character riganimation conducted by the simplified character rig animation module 126and vertex offset data. As discussed above, character animation dataexported by the data exporter module 112 and imported by the data importmodule 118 can include per-frame vertex offset data detailing per-frameoffsets for each vertex in a character model for every frame of ananimation. The vertex offset data can be used to simulate a complexcharacter rig deformation although only a simplified character rig hasactually been deformed in real-time. For every control vertex on thecharacter mesh, the complex model deformation module 128 can apply thesimplified character rig joint skinning to the modified joint hierarchyas well as any additional simplified character rig mesh deformers (suchas lattices or smoothing) and then apply the vertex offsets of thecomplex character rig mesh. This may involve blending between sets ofvertex offsets from different animations and/or layering vertex offsetsfrom different animations based on the animation drivers. More detailsregarding the complex model deformation module 128 will be providedbelow with reference to FIG. 4.

The model tessellation module 130 can be configured to tessellate andrender character models. Polygonal models can be tessellated andrendered, but this makes the final character deformations look rough andfaceted (a look traditionally associated with game characters). To makethe final models appear smooth and high fidelity (a look associated withfeature film characters), the model tessellation module 130 can, in oneembodiment, tessellate and render Catmull-Clark subdivision surfaces inreal-time. The model tessellation module 130 can tessellate onesubdivision iteration on the deformed complex model and calculate vertexnormals and tangents. This subdivided, tessellated model can then behanded off to the rendering pipeline module 132 for rasterization. Moredetails regarding the model tessellation module 130 will be providedbelow with reference to FIG. 5.

FIG. 2 illustrates an example data exporter module 202 according to anembodiment of the present disclosure. In some embodiments, the dataexporter module 112 of FIG. 1A can be implemented as the data exportermodule 202. As shown in the example of FIG. 2, the data exporter module202 can include an animation exporter module 204 and a data compressionmodule 206.

The animation exporter module 204 can be configured to generate andexport character animation data for use by a real-time processing engine(e.g., real-time engine module 116). In an embodiment, the animationexporter module 204 can receive at least two inputs: (1) world-spacepositions of all vertices of a complex character rig; and (2) asimplified character rig. In one embodiment, the simplified characterrig can include (a) a simplified skeleton representing a subset of theimportant deformation joints of the complex character rig with controljoints excluded; (b) a full-resolution copy of the character model; (c)a set of skinning weights that provide the relationship between theskeleton and the model; and (d) a skinning model (e.g., linear blendskinning or dual quaternion skinning). For each frame in an animation,and for each vertex in the model, the animation exporter module 204 canbe configured to calculate an offset between the complex character rigand the deformed result of the simplified character rig relative to theweights and influencing transform matrices of the simplified characterrig.

In an embodiment, the linear blend skinning equation (with four joints)may be represented as:

$v^{\prime} = {(v){\sum\limits_{n = 1}^{4}\; {{w_{n}\left( R_{n}^{- 1} \right)}\left( M_{n} \right)}}}$

where v′ is the deformed vertex position, vis the reference position ofthe vertex, w is the skinning weight for the given joint, R is thetransform of the given joint in its reference state, and M is thetransform of the given joint in the deformed state.

The summation can be factored out to produce a combined transform, S:

$S = {\sum\limits_{n = 1}^{4}\; {{w_{n}\left( R_{n}^{- 1} \right)}\left( M_{n} \right)}}$

The offset (o) can then be calculated:

o=v″(S ⁻¹)−v

where v″ is the world-space position of the vertex from the complexcharacter rig, S is the combined transform produced by the simplifiedcharacter rig, and vis the reference position of the vertex. It shouldbe noted that S can be produced by other skinning methods, such as dualquaternion skinning. S can also be generated from additional deformerssuch as lattices and smoothing operations.

The data compression module 206 can be configured to compress data to beexported by the data exporter module 202. The data exported by the dataexporter module 202 (e.g., character rig data, character animation data)has both spatial and temporal coherence, making it highly compressible.

In certain embodiments, tight bounding boxes can be calculated for eachvertex of a character model over an animation, allowing vertex offsetsto be quantized using low-order (e.g., 8 or 16 bit) integers whilemaintaining sub-millimeter accuracy. In an embodiment, the datacompression module 206 can be configured to calculate tight boundingboxes for each vertex in a model by tracking the minimum and maximum X,Y, and Z values of the offsets for the vertex throughout an animation.The vertex offsets can then be embedded into the bounding boxes usinglow-order integers. In certain embodiments, an 8-bit quantization may beused. In many instances, an 8-bit quantization shows no visible loss ofprecision, while providing a 4× improvement over a 32-bit singleprecision floating point representation of the data. In certainembodiments, the low-order integers may represent real-world distancesin a non-linear fashion so that they better capture vertex offsetdistributions across an animation where a bias is observed towards thecenter of the bounding box as compared to its faces (e.g. the 8-bitvalues are not interpreted as integers, but instead are treated asfloating point numbers with a 1-bit sign, 5-bit mantissa and 2-bitexponent). In certain embodiments, the low-order integers may representincremental real-world distances between successive animation frames(e.g. successive offsets with values 1, 2, 5, and 4 are represented as1, +1 (=2−1), +3 (=5−2), and −1 (=4−5)).

In various embodiments, the data compression module 206 can beconfigured to combine the 8-bit or 16-bit quantization of a characteranimation with lossy or lossless audio compression techniques, such asMP3 and WAV, by mapping the X, Y, and Z vertex offset values to threechannels of a single multi-channel audio stream. In various embodiments,the data compression module 206 can be configured to combine the 8-bitor 16-bit quantization of a character animation with lossy or losslessphoto compression techniques, such as JPEG and PNG, by mapping the X, Y,and Z vertex offset values to the R, G, and B color values of thephoto's pixels, and using adjacent pixels for sequential animationframes. In various embodiments, the data compression module 206 can beconfigured to combine the 8-bit or 16-bit quantization of a characteranimation with lossy or lossless video compression techniques, such asH.264, H.265, and VP9, by mapping the X, Y, and Z vertex offset valuesto the R, G, and B color values of the video frames. Many computingdevices used for AR and VR, such as mobile devices (e.g., phones,tablets) and computers, contain dedicated hardware to decode popularphoto, audio, and/or video compression formats while minimizingconsumption of CPU or GPU resources.

In various embodiments, the data compression module 206 can beconfigured to apply statistical analysis and machine learning techniquesto take advantage of the spatial and temporal coherence of the dataexported by the data exporter module 202. As one example implementation,Principal Component Analysis (PCA) can identify a lower dimensionalspace to project all the target poses (animation frame data within thecharacter animation data) such that the character animation data can berecreated within a given level of acceptable error (e.g., within 99%accuracy). A clustered PCA approach can be used in conjunction with aclustering algorithm like K-means to achieve better results. In certainembodiments, parametrized analytical approximate representations (e.g.,polynomials, sinusoidal functions, spline curves such as clamped cubicBezier curves) of vertex offsets over time provide an opportunity forsignificant savings through keyframe reduction techniques. In certainembodiments, spline curves approximately represent the vertex offsetsover time, and the parameters of those curves (e.g., the order of thespline, and the number and location of the spline's control points) arechosen so that the storage needed to store the parameters is minimizedthrough well-known curve-fitting algorithms. In certain embodiments, thenumber of control points is minimized by eliminating every control pointwhose removal does not cause the curve to deviate from the fittedoffsets beyond a specified acceptable threshold. In certain embodiments,the elimination of a spline control point is coupled with movingadjacent spline control points towards the position of the eliminatedpoint, to minimize the impact of the elimination to the shape of thespline curve.

In one embodiment, each vertex of a character model can map to a texturelocation using standard UV texture lookup techniques. This improves thespatial coherence of the data (and thus compressibility), but at theexpense of wasted texture memory storage space for pixels that don't mapto vertices. In another embodiment, a one-to-one correlation betweeneach vertex of a model and each pixel of a video stream can be madethrough an indexing scheme. In certain instances, the quality andcompressibility of this scheme may be dependent upon the indexing. Toensure better spatial coherence of the data, clustering and sorting maybe performed used statistical analysis. In one embodiment, the data maybe clustered using K-means. Then, PCA can be applied to each cluster,and the vertices can be sorted within a cluster by projecting thetemporal sequence onto its largest eigenvector.

FIG. 3 illustrates an example nonverbal character cues module 302according to an embodiment of the present disclosure. In someembodiments, the nonverbal character cues module 124 of FIG. 1A can beimplemented as the nonverbal character cues module 302. As discussedabove, the nonverbal character cues module 302 can be configured togenerate nonverbal communication animation data for effectuating one ormore nonverbal communication animations in a virtual character. Thenonverbal character cues module 302 can generate nonverbal communicationanimation data based on one or more user inputs and/or VR environmentstate information. As shown in the example of FIG. 3, the nonverbalcharacter cues module 302 can include an eye contact and looking module304, a mirroring module 306, an increased activity module 308, and adynamic performance module 310.

The eye contact and looking module 304 can be configured to generatelooking animation data based on user inputs and/or VR environment stateinformation. The looking animation data can be used (e.g., by thesimplified character rig animation module 126), to cause a virtualcharacter to look at a target location. One of the most powerful formsof nonverbal communication is eye contact, where a person looks atsomeone else and makes direct eye contact. This builds a sense ofengagement and connection between two people. However, prolonged eyecontact can sometimes lead to discomfort. In contrast, avoiding eyecontact can have the opposite effect, signaling disinterest, lack ofengagement, or even avoidance.

VR provides the audience with complete agency in a scene. They can lookanywhere and therefore may miss important parts of the story. VR contentcreators may find it desirable to direct the eyes of a viewer to theright place at the right time. When a virtual character procedurallyadjusts his or her eye direction to directly look at and follow a user,the user naturally responds by focusing his or her attention fully backat the character. The virtual character can maintain eye contact,suspend eye contact temporarily at regular intervals to preventdiscomfort but without altogether disengaging the user, or break eyecontact (for example to look up at the sky) and lead the user (who isalready looking at her) to focus on the next story beat. In anembodiment, the eye contact and looking module 304 can be configured togenerate looking animation data that causes a character to look at auser. When it is determined that the user is looking back at thecharacter, the eye contact and looking module 304 can be configured togenerate a second set of looking animation data that maintains eyecontact, suspends eye contact, or causes the character to look atanother object in order to direct the user to look at the other object.

In an embodiment, the eye contact and looking module 304 can beconfigured to adjust the eye(s), head(s), neck(s), and/or bodyconfiguration and location of a virtual character (e.g., adjustassociated vertices and/or joints of the character rig/model) so that acharacter can look directly at a target location such as the viewer'shead or hand locations or other objects. The eye contact and lookingmodule 304 can be configured to blend between any number of targetlocations over time to dynamically adjust a character's eye contact.

The eye contact and looking module 304 can be configured to use the HMDhead location of a user as an input in order to set the user's head asthe eye contact target for the virtual character. In an embodiment, theeye contact and looking module 304 can be configured to use both the HMDhead location and one or more hand controller locations as inputs sothat the virtual character can procedurally switch eye contact betweenthe user's head and the user's hands over time. In an embodiment, a setof features on the user's body and face are tracked using computervision and motion capture techniques. The eye contact and looking module304 can be configured to track any user feature that is tracked. Inanother embodiment, the user's eye direction is tracked independentlyfrom the head location and orientation. For example, a user may befacing in one direction and looking in another direction. The eyecontact and looking module 304 can be configured to track the user's eyedirection independently from the head position and orientation. In anembodiment, the eye contact and looking module 304 can be configured touse both the HMD head and hand controller locations of the user as wellas the locations of other characters (e.g., the heads of othercharacters) as inputs. This enables the virtual character to change eyecontact between the viewer and other characters in the scene. In anotherembodiment, the system uses multiple HMD head and hand controllerlocations associated with multiple users to enable eye contact by avirtual character between multiple users in a shared experience.

The eye contact and looking module 304 can be configured to adjust ananimation rig for a virtual character based on a target location. Thesimplest adjustment is to procedurally modify the eye joint location ofthe character based on the target location. However, this solution maylimit the range of eye contact that can be achieved, the realism of themovement, or the emotion conveyed by the character (e.g., extreme eyemovements without any head or neck movement may suggest the characterhas been immobilized or feels suspicion). In another embodiment, the eyecontact and looking module 304 can procedurally modify a series ofadditional joints (e.g., one or more joints in the body, neck, and headof the character) along with the eye joint location based on how far theeyes need to rotate in order to look at the target location. The eyecontact and looking module 304 can also be configured to procedurallymodify target locations so that a character can look at differentobjects over time.

In one example embodiment, the animation joints can be blended or lockeddirectly to the target location. This approach may, in certaininstances, be limited in that it removes any source animation present inthese animation joints and can remove important acting cues. In anotherembodiment, the various neck, head, and eye joints can be animatedrelative to a look at camera in the animation content creation tool. Inthe real-time processing engine, the eye contact and looking module 304can transform the look at camera to the target location and then blendeach neck, head, and eye joint between the default look at camera andthe target look at camera. This approach enables the character to lookat specific target locations but retain the original animatedperformance which is layered relative to the look at. This approach canalso be implemented in other interactive animations, such as mirroringand increased activity, as will be described in greater detail herein.

In one embodiment, the eye contact and looking module 304 can beconfigured to blend between a set (e.g., a plurality) of lookinganimations that modify the animation rig. This allows artists to finetune exactly how the character adjusts based on a target location. Italso enables animators to inject personality into the looking behavior.In another embodiment, the eye contact and looking module 304 can beconfigured to blend additional animations to capture nuanced performancesuch as eye darts.

The mirroring module 306 can be configured to generate mirroringanimation data based on user inputs and/or virtual environment stateinformation. The mirroring animation data can be used (e.g., by thesimplified character rig animation module 126), to cause a virtualcharacter to mirror the movement and actions of a user while stillproducing realistic character movement despite any differences in sizeor appearance between the character and the user. Another powerfulnonverbal cue is mimicry or mirroring. Two people will often mirror eachother's actions subconsciously without them even realizing it. Thishuman behavior builds connection, and is employed not only inentertainment contexts, but also in clinical or education contexts suchas therapy or teaching children. The mirroring module 306 enablesvirtual characters to mirror actions of a user to build subtleconnection with the user.

In one example, the mirroring module 306 can receive HMD position andorientation information as inputs and extract a specific behavior ofinterest from the head transform, such as a head tilt. The mirroringmodule 306 can generate mirroring animation data which causes thevirtual character to mirror the user's actions on a time delay, forexample on a 4 second delay. The mirroring module 306 can generatemirroring animation data which causes the virtual character to mimic thehead tilt of the user. In an embodiment, the head tilt transform can beprocedurally layered into the animation rig's head joint on a timedelay. In another example, the mirroring module 306 can use the HMDlocation and hand controller locations as inputs to procedurally modifythe animation rig's head and hand joints on a time delay. In oneembodiment, the input transforms can be used to blend between mirroringanimations that then modify the animation rig. For example, a head tiltanimation might incorporate facial features such as the charactersmiling as the head rotates. In one embodiment, the mirroring module 306can mirror the user's entire body movements based on full body tracking.In another embodiment, the mirroring module 306 can dynamically adjustthe time delay before mirroring user actions.

The increased activity module 308 can be configured to generate activitylevel animation data based on user inputs. The activity level animationdata can be used (e.g., by the simplified character rig animation module126), to cause an adjustment in an activity level of a virtualcharacter. Increased activity levels may indicate positive feelings suchas interest and excitement, negative ones such as nervousness, stress,and fear, or be an autonomic response to environmental factors such aslow temperature. In children this excitement level is quite apparent.Even excited adults fidget more, talk more, and talk more quickly.Rising activity level can be a subconscious cue that two adults areexploring the possibility of a closer relationship. The increasedactivity module 308 enables content creators to procedurally layerincreased activity based on state information for the VR experience.This behavior can be layered procedurally by speeding up the intensityof an animation or speeding up character speech to adjust the animationrig joints. In another embodiment, the increased activity is achieved byblending specific animations such as fidgeting behavior or nervousness.

The dynamic performance module 310 can be configured to make dynamicadjustments to character performances based on user inputs and/orvirtual environment state information. For example, the dynamicperformance module 310 can be configured to adjust character stagingbased on user position information so that the user can always see theperformance clearly. As another example, a user can trigger layeredcharacter responses such as making a character appear to feel cold. Inthis example, the dynamic performance module 310 can cause a shiveringanimation to be layered on top of a character's existing action.

In additional examples, the dynamic performance module 310 can beconfigured to enable characters to adjust their facial performances asneeded, such as adding winks, eye blinks, and/or raising their eyebrowsat a user. Procedural nonverbal adjustments can be made at specificpoints in a narrative or based on user response, such as user gaze oruser touch.

The dynamic performance module 310 can support adjusting and/or swappingprops that a character manipulates or uses during a performance. Thedynamic performance module 310 can swap a prop for a piece of contentbased on external information about the user. This can be used todynamically adjust or change props for product placement in a piece ofcontent. Dynamic props can also personalize a piece of content based onprior knowledge about a user. Props can include, for example, clothing,and other elements of the character's appearance such as hair or skincolor.

FIG. 4 illustrates an example complex model deformation module 402according to an embodiment of the present disclosure. In someembodiments, the complex model deformation module 128 of FIG. 1A can beimplemented as the complex model deformation module 402. As shown in theexample of FIG. 4, the complex model deformation module 402 can includea data reconstruction module 404 and a skin compute shader module 406.

The data reconstruction module 404 can be configured to, at runtime,decompress any necessary character animation data (e.g., vertexanimation data), blend stored offsets using weights calculated duringanimation of the simplified character rig (e.g., by the animation module110), and apply skinning transformations. This may be done, for example,using compute shaders, making it highly performant. If statisticalmethods, such as PCA, have been applied to the character animation data,then the data reconstruction module 404 can be configured to reconstructthe full set of vertices from the lower dimensional data space.

The skin compute shader module 406 can be configured to reconstructfull-quality character (i.e., complex character) deformations based onsimplified character deformations for a given vertex as follows:

$v^{''} = {\left( {v + {\sum\limits_{n}^{\;}\; {x_{n}\left( o_{n} \right)}}} \right)S}$

where v″ is the reconstructed world-space position of the vertex fromthe full-quality, complex character rig, S is the combined transformproduced by the simplified character rig, v is the reference position ofthe model, xis the weight of the given animation clip, and o is theoffset of the vertex at the given frame of the animation clip. Theresult is a reconstruction of the data provided by the data exportermodule 202 but with procedural animation applied from the simplifiedcharacter rig animation module 126. This reconstruction is perfect if nocompression was used by the data exporter module 202, or if losslesscompression was used; otherwise, if lossy compression was used, thereconstruction is a close approximation.

FIG. 5 illustrates an example model tessellation module 502 according toan embodiment of the present disclosure. In some embodiments, the modeltessellation module 130 of FIG. 1A can be implemented as the modeltessellation module 502. As shown in the example of FIG. 5, the modeltessellation module 502 can include a subdiv tessellator module 504 anda normal and tangent calculation module 506.

The subdiv tessellator module 504 can be configured to smooth and refinecharacter animation geometry to generate a refined mesh. In oneembodiment, the subdiv tessellator module 504 can smooth characteranimation geometry using Catmull-Clark subdivision surfaces. Stencilweights can be precomputed for all subdivided vertex positions, makingthe real-time calculation a simple linear combination of the hull vertexpositions. In an embodiment, the subdiv tessellator module 504 can beconfigured to smooth and refine character animation geometry using arecursive refinement technique that produces progressively smootherversions of a mesh. In each iteration:

-   -   Each face of the mesh produces an additional vertex (F) at its        centroid;    -   Each edge of the mesh produces an additional vertex (E) as a        weighted combination of its centroid and the centroids of the        incident faces (F); and    -   Each vertex (V) of the mesh is placed at a weighted combination        of its original position, the centroids of incident edges (E),        and the centroids of incident faces (F).        FIG. 6 illustrates an example scenario 600 depicting two sample        iterations of the recursive technique described above. An        initial (or base) mesh 602 is refined in a first iteration to        generate a first refined mesh 604, which is further refined in a        second iteration to generate a second refined mesh 606. It can        be seen that each iteration results in a greater number of        vertices, faces, and edges, resulting in a progressively more        refined mesh. The dependency of (V) on (F) and (E), and of (E)        on (F), can be simplified by back-substitution. By applying this        substitution, any vertex in any subdivision level of a refined        mesh can be represented as a linear weighted combination of the        vertices in the deformed base mesh. The weights used in the        linear combination depend only on the base mesh topology, which        remains constant at runtime. As such, the weights can be        computed once as a pipeline step and stored. Although the number        of weights used to represent a particular vertex in a refined        mesh may be limited only by the number of vertices in the base        mesh, in certain embodiments, the number may be tied to the        valence of the vertex and the number of vertices in each        incident face. In certain embodiments, a hard limit may be set.        For example, each vertex in a refined mesh may be represented by        no more than 10 weights. In certain embodiments, the retained        weights may be the ones with the largest absolute magnitudes.        For example, each vertex may be represented by the 10 largest        weights.

Returning to FIG. 5, the normal and tangent calculation module 506 canbe configured to calculate approximate normal and tangent vectors foreach vertex in a mesh (e.g., a refined mesh or a base mesh) by using ananalog of the dual-graph of the mesh. This approach produceshigh-quality normals and tangents, even on arbitrarily deformedgeometry.

In a piecewise mesh with ordered vertices around each face, geometricnormals are implicitly defined on the mesh faces. However, to give theappearance of a smooth mesh, each vertex can be assigned a normal assome linear combination of the normals of the incident faces, and thenthese vertex normals can be linearly interpolated across a polygon usingbarycentric weights. However, this technique is computationallyexpensive since it requires the precomputation of the face normals ofall incident faces to a given vertex. As a result, real-time gameengines rarely calculate vertex normals from the deformed vertexpositions. Instead, they are typically calculated once in the referencepose, and then deformed using the skinning weights and transforms.

In an embodiment, the normal and tangent calculation module 506 can beconfigured to approximate vertex normals using four evenly-spacedadjacent vertices. For quad-dominant meshes, such as those typicallyused with Catmull-Clark subdivisions, most vertices have exactly fourneighbors, making the choice of the four adjacent vertices A, B, C, andD simple and unambiguous. FIG. 7 illustrates an example scenario 700 inwhich a vertex V has exactly four neighbors, A, B, C, and D. In suchscenarios, the normal can be calculated as follows:

${Normal} = \frac{\left( {A - C} \right) \times \left( {B - D} \right)}{{\left( {A - C} \right) \times \left( {B - D} \right)}}$

For extraordinary vertices with more than four adjacent vertices, thenormal and tangent calculation module 506 can be configured to selectfour evenly-spaced adjacent vertices from a set of adjacent vertices. Inone embodiment, the four adjacent vertices may be chosen using onlytopological considerations. For example, if a vertex has eight incidentedges, every other adjacent vertex could be used. In one embodiment, thefour adjacent vertices may be chosen using intrinsic properties of themesh. For example, the four adjacent vertices can be chosen such thatthe angle formed at the vertex by the two edges connecting the vertex toany two chosen sequential adjacent vertices is as close to 90 degrees aspossible. FIG. 8 illustrates an example scenario 800 in which a vertex Vhas more than four adjacent vertices. In the example scenario 800, thevertex V has eight adjacent vertices, and the four adjacent vertices A,B, C, and D have been selected by selecting every other adjacent vertex.

For border vertices in which a vertex has fewer than four adjacentvertices, the normal and tangent calculation module 506 can beconfigured to substitute the vertex itself in place of any missingvertices. FIG. 9 illustrates an example scenario 900 in which a vertex Vhas fewer than four adjacent vertices. In the example scenario 900, thevertex V has only three adjacent vertices, A, B, and C. As such, thevertex V, itself, is substituted as the fourth adjacent vertex, D.

In an embodiment, tangent vectors may follow a similar construction.Although an infinite number of tangent vectors exist for a given vertex(any vector that is perpendicular to the defined normal), in oneembodiment, the normal and tangent calculation module 506 can beconfigured to select the tangent vector that aligns with the U directionof a UV parameterization of the mesh. Conveniently, in the disclosedrepresentation, this tangent vector is a linear combination of the basisvectors (A−C) and (B−D). The coefficients depend only on the topologyand the UV parameterization, which remain fixed at run-time, allowingthe weights of the linear combination to be pre-computed as a pipelinestep. In an embodiment, the tangent vector can be calculated as follows:

${Tangent} = \frac{{\left( {A_{V} - C_{V}} \right)\left( {A - C} \right)} + {\left( {D_{V} - B_{V}} \right)\left( {B - D} \right)}}{{{\left( {A_{V} - C_{V}} \right)\left( {A - C} \right)} + {\left( {D_{V} - B_{V}} \right)\left( {B - D} \right)}}}$

A_(v), B_(v), C_(v), D_(v) represent the V component of the UVparametrization of the mesh at vertices A, B, C, and D.

Vertex positions for a refined mesh, as determined by the subdivtessellator module 504, and approximate normal and tangent vectors foreach vertex in the refined mesh, as calculated by the normal and tangentcalculation module 506, are passed to vertex shaders where they can beconsumed by the render pipeline module 132 of FIG. 1A.

FIG. 10 details an exemplary process for generating the vertexpositions, normal, and tangents for a Catmull-Clark subdivision surfacebased on a polygonal control model. This pipeline is optimized forreal-time computation on a GPU.

FIG. 11 illustrates an example method 1100, according to an embodimentof the present technology. At block 1102, the method 1100 can receivevirtual model information associated with a virtual deformable geometricmodel, the virtual model information comprising a complex rig comprisinga first plurality of transforms and a first plurality of verticesdefined by a default model, and a simplified rig comprising a secondplurality of transforms and a second plurality of vertices, wherein thesecond plurality of vertices correspond to the first plurality ofvertices defined by the default model. At block 1104, the method 1100can deform the simplified rig and the complex rig based on an animationto be applied to the virtual deformable geometric model. At block 1106,the method 1100 can calculate a set of offset data, the set of offsetdata comprising, for each vertex in the first plurality of vertices, anoffset between the vertex and a corresponding vertex in the secondplurality of vertices.

FIG. 12 illustrates an example method 1200, according to an embodimentof the present technology. At block 1202, the method 1200 can receivevirtual model information associated with a virtual deformable geometricmodel, the virtual model information comprising a complex rig comprisinga first plurality of transforms and a first plurality of verticesdefined by a default model, and a simplified rig comprising a secondplurality of transforms and a second plurality of vertices, wherein thesecond plurality of vertices correspond to the first plurality ofvertices defined by the default model. At block 1204, the method 1200can deform the simplified rig and the complex rig based on an animationto be applied to the virtual deformable geometric model. At block 1206,the method 1200 can calculate a set of offset data, the set of offsetdata comprising, for each vertex in the first plurality of vertices, anoffset between the vertex and a corresponding vertex in the secondplurality of vertices. At block 1208, the method 1200 can export acompressed version of the set of offset data to a real-time processingengine for real-time animation of the virtual deformable geometricmodel.

FIG. 13 illustrates an example method 1300, according to an embodimentof the present technology. At block 1302, the method 1300 can identify avirtual deformable geometric model to be animated in a real-timeimmersive environment, the virtual deformable geometric model comprisinga virtual model mesh comprising a plurality of vertices, a plurality ofedges, and a plurality of faces. At block 1304, the method 1300 caniteratively refine the virtual model mesh in one or more iterations togenerate a refined mesh, wherein each iteration of the one or moreiterations increases the number of vertices, the number of edges, and/orthe number of faces. At block 1306, the method 1300 can present therefined mesh during real-time animation of the virtual deformablegeometric model within the real-time immersive environment.

FIG. 14 illustrates an example method 1400, according to an embodimentof the present technology. At block 1402, the method 1400 can identify avirtual character being presented to a user within a real-time immersiveenvironment. At block 1404, the method 1400 can determine a firstanimation to be applied to the virtual character. At block 1406, themethod 1400 can determine a nonverbal communication animation to beapplied to the virtual character simultaneously with the firstanimation. At block 1408, the method 1400 can animate the virtualcharacter in real-time based on the first animation and the nonverbalcommunication animation.

Many variations to the example methods are possible. It should beappreciated that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments discussed herein unless otherwisestated.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 15 illustrates anexample machine 1500 within which a set of instructions for causing themachine to perform one or more of the embodiments described herein canbe executed, in accordance with an embodiment of the present disclosure.The embodiments can relate to one or more systems, methods, or computerreadable media. The machine may be connected (e.g., networked) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment.

The computer system 1500 includes a processor 1502 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU), or both), amain memory 1504, and a nonvolatile memory 1506 (e.g., volatile RAM andnon-volatile RAM, respectively), which communicate with each other via abus 1508. The processor 1502 can be implemented in any suitable form,such as a parallel processing system. In some cases, the example machine1500 can correspond to, include, or be included within a computingdevice or system. For example, in some embodiments, the machine 1500 canbe a desktop computer, a laptop computer, personal digital assistant(PDA), an appliance, a wearable device, a camera, a tablet, or a mobilephone, etc. In one embodiment, the computer system 1500 also includes avideo display 1510, an alphanumeric input device 1512 (e.g., akeyboard), a cursor control device 1514 (e.g., a mouse), a drive unit1516, a signal generation device 1518 (e.g., a speaker) and a networkinterface device 1520.

In one embodiment, the video display 1510 includes a touch sensitivescreen for user input. In one embodiment, the touch sensitive screen isused instead of a keyboard and mouse. The disk drive unit 1516 includesa machine-readable medium 1522 on which is stored one or more sets ofinstructions 1524 (e.g., software) embodying any one or more of themethodologies or functions described herein. The instructions 1524 canalso reside, completely or at least partially, within the main memory1504 and/or within the processor 1502 during execution thereof by thecomputer system 1500. The instructions 1524 can further be transmittedor received over a network 1540 via the network interface device 1520.In some embodiments, the machine-readable medium 1522 also includes adatabase 1525.

Volatile RAM may be implemented as dynamic RAM (DRAM), which requirespower continually in order to refresh or maintain the data in thememory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system that maintains data even after power is removedfrom the system. The non-volatile memory 1506 may also be a randomaccess memory. The non-volatile memory 1506 can be a local devicecoupled directly to the rest of the components in the computer system1500. A non-volatile memory that is remote from the system, such as anetwork storage device coupled to any of the computer systems describedherein through a network interface such as a modem or Ethernetinterface, can also be used.

While the machine-readable medium 1522 is shown in an exemplaryembodiment to be a single medium, the term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” shall also be taken to include any medium thatis capable of storing, encoding or carrying a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, optical and magnetic media, andcarrier wave signals. The term “storage module” as used herein may beimplemented using a machine-readable medium.

In general, routines executed to implement the embodiments of theinvention can be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “programs” or “applications”. For example,one or more programs or applications can be used to execute any or allof the functionality, techniques, and processes described herein. Theprograms or applications typically comprise one or more instructions setat various times in various memory and storage devices in the machineand that, when read and executed by one or more processors, cause thecomputing system 1500 to perform operations to execute elementsinvolving the various aspects of the embodiments described herein.

The executable routines and data may be stored in various places,including, for example, ROM, volatile RAM, non-volatile memory, and/orcache memory. Portions of these routines and/or data may be stored inany one of these storage devices. Further, the routines and data can beobtained from centralized servers or peer-to-peer networks. Differentportions of the routines and data can be obtained from differentcentralized servers and/or peer-to-peer networks at different times andin different communication sessions, or in a same communication session.The routines and data can be obtained in entirety prior to the executionof the applications. Alternatively, portions of the routines and datacan be obtained dynamically, just in time, when needed for execution.Thus, it is not required that the routines and data be on amachine-readable medium in entirety at a particular instance of time.

While embodiments have been described fully in the context of computingsystems, those skilled in the art will appreciate that the variousembodiments are capable of being distributed as a program product in avariety of forms, and that the embodiments described herein applyequally regardless of the particular type of machine- orcomputer-readable media used to actually effect the distribution.Examples of machine-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices,floppy and other removable disks, hard disk drives, optical disks (e.g.,Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks,(DVDs), etc.), among others, and transmission type media such as digitaland analog communication links.

Alternatively, or in combination, the embodiments described herein canbe implemented using special purpose circuitry, with or without softwareinstructions, such as using Application-Specific Integrated Circuit(ASIC) or Field-Programmable Gate Array (FPGA). Embodiments can beimplemented using hardwired circuitry without software instructions, orin combination with software instructions. Thus, the techniques arelimited neither to any specific combination of hardware circuitry andsoftware, nor to any particular source for the instructions executed bythe data processing system.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the descriptionor discussed herein. In other instances, functional block diagrams andflow diagrams are shown to represent data and logic flows. Thecomponents of block diagrams and flow diagrams (e.g., modules, engines,blocks, structures, devices, features, etc.) may be variously combined,separated, removed, reordered, and replaced in a manner other than asexpressly described and depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “another embodiment”, “in various embodiments,” orthe like means that a particular feature, design, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the disclosure. The appearances of, forexample, the phrases “according to an embodiment”, “in one embodiment”,“in an embodiment”, “in various embodiments,” or “in another embodiment”in various places in the specification are not necessarily all referringto the same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, whether or not thereis express reference to an “embodiment” or the like, various featuresare described, which may be variously combined and included in someembodiments but also variously omitted in other embodiments. Similarly,various features are described which may be preferences or requirementsfor some embodiments but not other embodiments.

Although embodiments have been described with reference to specificexemplary embodiments, it will be evident that the various modificationsand changes can be made to these embodiments. Accordingly, thespecification and drawings are to be regarded in an illustrative senserather than in a restrictive sense. The foregoing specification providesa description with reference to specific exemplary embodiments. It willbe evident that various modifications can be made thereto withoutdeparting from the broader spirit and scope as set forth in thefollowing claims. The specification and drawings are, accordingly, to beregarded in an illustrative sense rather than a restrictive sense.

Although some of the drawings illustrate a number of operations ormethod steps in a particular order, steps that are not order dependentmay be reordered and other steps may be combined or omitted. While somereordering or other groupings are specifically mentioned, others will beapparent to those of ordinary skill in the art and so do not present anexhaustive list of alternatives. Moreover, it should be recognized thatthe stages could be implemented in hardware, firmware, software or anycombination thereof.

It should also be understood that a variety of changes may be madewithout departing from the essence of the invention. Such changes arealso implicitly included in the description. They still fall within thescope of this invention. It should be understood that this disclosure isintended to yield a patent covering numerous aspects of the invention,both independently and as an overall system, and in both method andapparatus modes.

Further, each of the various elements of the invention and claims mayalso be achieved in a variety of manners. This disclosure should beunderstood to encompass each such variation, be it a variation of anembodiment of any apparatus embodiment, a method or process embodiment,or even merely a variation of any element of these.

Further, the use of the transitional phrase “comprising” is used tomaintain the “open-end” claims herein, according to traditional claiminterpretation. Thus, unless the context requires otherwise, it shouldbe understood that the term “comprise” or variations such as “comprises”or “comprising”, are intended to imply the inclusion of a stated elementor step or group of elements or steps, but not the exclusion of anyother element or step or group of elements or steps. Such terms shouldbe interpreted in their most expansive forms so as to afford theapplicant the broadest coverage legally permissible in accordance withthe following claims.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a computing system, virtual model information associatedwith a virtual deformable geometric model, the virtual model informationcomprising a complex rig comprising a first plurality of transforms anda first plurality of vertices defined by a default model, and asimplified rig comprising a second plurality of transforms and a secondplurality of vertices, wherein the second plurality of verticescorrespond to the first plurality of vertices defined by the defaultmodel; deforming, by the computing system, the simplified rig and thecomplex rig based on an animation to be applied to the virtualdeformable geometric model; calculating, by the computing system, a setof offset data, the set of offset data comprising, for each vertex inthe first plurality of vertices, an offset between the vertex and acorresponding vertex in the second plurality of vertices; and exporting,by the computing system, a compressed version of the set of offset datato a real-time processing engine for real-time animation of the virtualdeformable geometric model.
 2. The computer-implemented method of claim1, further comprising generating the compressed version of the set ofoffset data.
 3. The computer-implemented method of claim 2, wherein thegenerating the compressed version of the set of offset data comprisescalculating a tight bounding box for each vertex of the first pluralityof vertices by, for each vertex of the first plurality of vertices,tracking minimum and maximum X, Y, and Z values of offsets for thevertex through the animation.
 4. The computer-implemented method ofclaim 3, wherein the generating the compressed version of the set ofoffset data further comprises quantizing offsets in the set of offsetdata using integers of 16-bits or less based on the tight bounding boxesto generate a lower bit quantization of offset data.
 5. Thecomputer-implemented method of claim 4, wherein the generating thecompressed version of the set of offset data further comprises combiningthe lower bit quantization of offset data with a video compressiontechnique by mapping X, Y, and Z offset values to color componentvalues.
 6. The computer-implemented method of claim 4, wherein thegenerating the compressed version of the set of offset data furthercomprises combining the lower bit quantization of offset data with lossyor lossless audio compression techniques by mapping the X, Y, and Zoffset values to channels of an audio stream.
 7. Thecomputer-implemented method of claim 4, wherein the generating thecompressed version of the set of offset data further comprises combiningthe lower bit quantization of offset data with lossy or lossless photocompression techniques by mapping the X, Y, and Z offset values to thecolor component values of pixels in a photo and using adjacent pixelsfor sequential animation frames.
 8. The computer-implemented method ofclaim 2, wherein the generating the compressed version of the set ofoffset data further comprises correlating each vertex of the firstplurality of vertices to a pixel of a video stream.
 9. Thecomputer-implemented method of claim 8, wherein the correlating eachvertex of the first plurality of vertices to a pixel of a video streamcomprises correlating each vertex of the first plurality of vertices toa pixel of a video stream by mapping each vertex of the first pluralityof vertices to a texture location using a texture lookup or by using anindexing scheme.
 10. The computer-implemented method of claim 2, whereinthe generating the compressed version of the set of offset datacomprises clustering the set of offset data to generate a plurality ofclusters, and further wherein the clustering the set of offset datacomprises clustering the set of offset data using K-means.
 11. Thecomputer-implemented method of claim 2, wherein the generating thecompressed version of the set of offset data comprises clustering theset of offset data to generate a plurality of clusters; and applyingPrincipal Component Analysis (PCA) to each cluster of the plurality ofclusters.
 12. The computer-implemented method of claim 2, wherein thegenerating the compressed version of the set of offset data comprisesrepresenting changes across time for offsets of the set of offset datausing an approximating parametrized analytical function; and retainingonly a subset of the parameters of the approximating parametrizedanalytical function.
 13. A system comprising: at least one processor;and a memory storing instructions that, when executed by the at leastone processor, cause the system to perform a method comprising:receiving virtual model information associated with a virtual deformablegeometric model, the virtual model information comprising a complex rigcomprising a first plurality of transforms and a first plurality ofvertices defined by a default model, and a simplified rig comprising asecond plurality of transforms and a second plurality of vertices,wherein the second plurality of vertices correspond to the firstplurality of vertices defined by the default model; deforming thesimplified rig and the complex rig based on an animation to be appliedto the virtual deformable geometric model; calculating a set of offsetdata, the set of offset data comprising, for each vertex in the firstplurality of vertices, an offset between the vertex and a correspondingvertex in the second plurality of vertices; and exporting a compressedversion of the set of offset data to a real-time processing engine forreal-time animation of the virtual deformable geometric model.
 14. Thesystem of claim 13, wherein the instructions, when executed by the atleast one processor, further cause the system to perform generating thecompressed version of the set of offset data.
 15. The system of claim14, wherein the generating the compressed version of the set of offsetdata comprises calculating a tight bounding box for each vertex of thefirst plurality of vertices by, for each vertex of the first pluralityof vertices, tracking minimum and maximum X, Y, and Z values of offsetsfor the vertex through the animation.
 16. The system of claim 15,wherein the generating the compressed version of the set of offset datafurther comprises quantizing offsets in the set of offset data usingintegers of 16-bits or less based on the tight bounding boxes togenerate a lower bit quantization of offset data.
 17. A non-transitorycomputer-readable storage medium including instructions that, whenexecuted by at least one processor of a computing system, cause thecomputing system to perform a method comprising: receiving virtual modelinformation associated with a virtual deformable geometric model, thevirtual model information comprising a complex rig comprising a firstplurality of transforms and a first plurality of vertices defined by adefault model, and a simplified rig comprising a second plurality oftransforms and a second plurality of vertices, wherein the secondplurality of vertices correspond to the first plurality of verticesdefined by the default model; deforming the simplified rig and thecomplex rig based on an animation to be applied to the virtualdeformable geometric model; calculating a set of offset data, the set ofoffset data comprising, for each vertex in the first plurality ofvertices, an offset between the vertex and a corresponding vertex in thesecond plurality of vertices; and exporting a compressed version of theset of offset data to a real-time processing engine for real-timeanimation of the virtual deformable geometric model.
 18. Thenon-transitory computer-readable storage medium of claim 17, wherein theinstructions, when executed by the at least one processor of thecomputing system, further cause the computing system to performgenerating the compressed version of the set of offset data.
 19. Thenon-transitory computer-readable storage medium of claim 18, wherein thegenerating the compressed version of the set of offset data comprisescalculating a tight bounding box for each vertex of the first pluralityof vertices by, for each vertex of the first plurality of vertices,tracking minimum and maximum X, Y, and Z values of offsets for thevertex through the animation.
 20. The non-transitory computer-readablestorage medium of claim 19, wherein the generating the compressedversion of the set of offset data further comprises quantizing offsetsin the set of offset data using integers of 16-bits or less based on thetight bounding boxes to generate a lower bit quantization of offsetdata.